performing-proteomics-quality-control

name	performing-proteomics-quality-control
description	Perform quality control for protein-level proteomics data. Use when filtering outlier samples or low-evidence features before downstream analysis. Covers PSM artifact removal, sample QC, feature-level filtering. Does NOT cover normalization, batch correction, or imputation.

Performing Proteomics Quality Control

Goal: Remove outlier samples and unsupported features from protein-level quantification matrices.

Context and Definitions

Key Metrics

• Decoy prefixes: REV_, DECOY_, or boolean decoy indicator column
• Quality Control (QC) for technical replicates: Remove technical replicates when coefficient of Variation (CV) > 20-30% or Pearson Correlation R < 0.9
• Median absolute deviation (MAD)-based outlier detection: An observation x from a set of observations X is flagged as an outlier when |x - median(X)| > N × MAD(X), where N is a user-defined factor. MAD is calculated as the median of absolute deviations from the median: MAD(X) = median(|X - median(X)|).
• Feature Completeness: Feature-wise fraction of samples with non-missing values.

Study Types

Type	Min features/sample	Completeness	Intensity metric
Single-cell	>500-600 proteins	10-15%	Total intensity
Bulk tissue	No strict minimum	50-70%	Total intensity
Plasma/serum	No strict minimum	20-50%	Median intensity

Defaults

Parameter	Default	Adjust when
False discovery threshold	0.01	Standard value
MAD multiplier (N)	3	Lower (<3) strict, higher (>3) more permissive
Min unique peptides	2	>2 for validation studies
Feature Completeness	Study-dependent, typically between 10% - 70%	Must find a compromise between robustness and expected biological prevalence. If an effect is expected in a small subset of samples (e.g. a rare cell type) it should not be removed by feature completeness filters

Failed Sample Detection

Removing failed samples is critical. Samples with very low protein counts or intensity typically result from missed injections, sample degradation, or other laboratory errors. Since proteomics relies on label-free quantification (relative quantities across samples), failed samples corrupt normalization and distort all downstream results.

Example: In a plasma study, one sample has 30 protein groups while all others have ~500 protein groups—this outlier should be removed before normalization.

Checklist

1. Clarify Parameters
2. Remove Low Confident Identifications
3. Remove Peptide Spectrum Match Artifacts
4. If technical replicates exist: Assess technical replicate reproducibility
5. Remove sample outliers
6. Filter features below completeness threshold
7. Generate QC report

Order matters: Steps 2-3 (PSM) → Steps 4-6 (Sample QC) → Step 7 (Feature QC). Sample QC must precede feature QC because outlier samples inflate apparent missingness.

Instructions

Step 1: Clarify Parameters

Before starting, check for:

• Study type: Single-cell, bulk, or plasma?
• Completeness threshold: What minimum biological prevalence is expected? (e.g., 10% cell population → expect 90% missingness for its markers)
• Outlier handling: Remove samples or flag only?
• Technical replicates: Present in dataset?

Step 2: Remove Low Confident Identifications

• Remove rows with decoy prefixes or decoy indicator = True
• If applicable: Remove proteins with < 2 unique peptides

Step 3: Remove Peptide Spectrum Match Artifacts

• Remove rows with false discovery rates >= false discovery threshold

Step 4: Technical Replicate QC (if applicable)

• Compute CV and Pearson R between replicates
• Remove replicate sets with CV > 30% or Pearson R < 0.9

Step 5: Sample QC

Compute per-sample:

• Intensity: Total (or median for plasma/serum)
• Feature count: Number of non-missing proteins

For each metric, remove MAD outliers (see Key Metrics)

Always perform analysis and remove outliers before proceeding.

Step 6: Feature QC

• Compute completeness per feature
• Remove features below completeness threshold

Step 7: Generate QC Report

Output:

1. Summary table: That shows for each quality control step the number of samples and features before and after filtering. For an example see ./references/qc-summary.tsv
2. Histogram of each metric: Distribution of the respective metric before filtering on the left, and after filtering on the right.
3. Filtered matrix: With observations as rows and features as columns. Ready for downstream processing